A classifier often functions as an output layer, for example in deep learning networks for image processing and pattern recognition. In a pattern recognition application, a classifier is trained in a supervised way, in which expected outputs are provided along with the input images. Once the training process is completed, the classifier is tested with a different set of image data for how well it can recognize similar patterns. We trained and tested our mem-device crossbar classifiers with two typical datasets: MNIST [1]} and CIFAR-10 [2]}. The MNIST dataset contains handwritten digits of size \(28\times 28\) . This dataset has 60,000 training and 10,000 testing images. The CIFAR-10 dataset is a collection of 60,000 color images of size of \(32\times 32\) , which is divided into 50,000 training and 10,000 testing images. There are 10 different classes of objects.
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